A Social Relationship Direction Gating Algorithm for Graph Convolutional Networks

doi:10.13190/j.jbupt.2020-074

Journal of Beijing University of Posts and Telecommunications ›› 2020, Vol. 43 ›› Issue (5): 77-83.doi: 10.13190/j.jbupt.2020-074

• PAPERS • Previous Articles Next Articles

A Social Relationship Direction Gating Algorithm for Graph Convolutional Networks

LI Lei, XIE Yang, JIANG Ya-fei, LIU Yong-bin

School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2020-06-25 Published:2021-03-11

Abstract

Abstract: Facing the problem in social user attitude analysis that the original social relationship direction between users in social networks hinders the flow of attitude information and label propagation, a social relationship direction gating algorithm for graph convolutional networks is proposed. The algorithm first performs graph convolution on the origin and reverse social relationship directions to obtain two types of user node attitude feature vectors, and then leverages the gating mechanism to integrate the feature vectors dynamically. While expanding the propagation of attitude information, the algorithm can also capture the differences of user influence to automatically select the flow of attitude information. Experiments on two real hot topic datasets show that the accuracy of the existing graph convolutional networks can be effectively improved after adding the proposed algorithm.

Key words: graph convolutional network, gating algorithm, user attitude analysis, social network

CLC Number:

TP391

LI Lei, XIE Yang, JIANG Ya-fei, LIU Yong-bin. A Social Relationship Direction Gating Algorithm for Graph Convolutional Networks[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(5): 77-83.

References

[1] Bing Liu. Sentiment analysis and opinion mining[J]. Synthesis Lectures on Human Language Technologies, 2012, 5(1):1-167.[LinkOut]
[2] Myers D G. Social psychology[M]. New York:Mcgraw-Hill, 2009:124.
[3] 李勇敢, 周学广, 孙艳, 等. 中文微博情感分析研究与实现[J]. 软件学报, 2017, 28(12):3183-3205. Li Yonggan, Zhou Xueguang, Sun Yan, et al. Research and implementation of Chinese microblog sentiment classification[J]. Journal of Software, 2017, 28(12):3183-3205.
[4] 李婷婷, 姬东鸿. 基于SVM和CRF多特征组合的微博情感分析[J]. 计算机应用研究, 2015, 32(4):978-981. Li Tingting, Ji Donghong. Sentiment analysis of micro-blog based on SVM and CRF using various combinations of features[J]. Application Research of Computers, 2015, 32(4):978-981.
[5] 周瑛, 刘越, 蔡俊. 基于注意力机制的微博情感分析[J]. 情报理论与实践, 2018, 41(3):89-94. Zhou Ying, Liu Yue, Cai Jun. Sentiment analysis of micro-blogs based on attention mechanism[J]. Information Studies (Theory & Application), 2018, 41(3):89-94.
[6] 杜慧, 俞晓明, 刘悦, 等. 融合词性和注意力的卷积神经网络对象级情感分类方法[J]. 模式识别与人工智能, 2018, 31(12):1120-1126. Du Hui, Yu Xiaoming, Liu Yue, et al. CNN with part-of-speech and attention mechanism for targeted sentiment classification[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(12):1120-1126.
[7] 孙晓, 彭晓琪, 胡敏, 等. 基于多维扩展特征与深度学习的微博短文本情感分析[J]. 电子与信息学报, 2017, 39(9):2048-2055. Sun Xiao, Peng Xiaoqi, Hu Min, et al. Extended multi-modality features and deep learning based microblog short text sentiment analysis[J]. Journal of Electronics & Information Technology, 2017, 39(9):2048-2055.
[8] West R, Paskov H S, Leskovec J, et al. Exploiting social network structure for person-to-person sentiment analysis[J]. Transactions of the Association for Computational Linguistics, 2014, 2:297-310.
[9] 黄丹阳, 王菲菲, 杨扬, 等. 基于网络结构与用户内容的动态兴趣识别方法[J]. 北京邮电大学学报, 2018, 41(2):103-108. Huang Danyang, Wang Feifei, Yang Yang, et al. Dynamic interest identification based on social network structure and user generated contents[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(2):103-108.
[10] Zhu X, Ghahramani Z, Lafferty J D. Semi-supervised learning using gaussian fields and harmonic functions[C]//Proceedings of the 20th International Conference on Machine Learning (ICML-03). Washington, DC:AAAI Press, 2003:912-919.
[11] Chapelle O, Zien A. Semi-supervised classification by low density separation[C]//AISTATS.2005:57-64.
[12] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[EB/OL]. 2016(2016-09-06)[2020-06-25]. https://arxiv.org/abs/1609.02907.
[13] Hamilton W L, Ying R, Leskovec J. Inductive representation learning on large graphs[EB/OL]. 2017(2017-06-07)[2020-06-25]. https://arxiv.org/abs/1706.02216.
[14] Veličković P, Cucurull G, Casanova A, et al. Graph attention networks[EB/OL]. 2017(2017-10-30)[2020-06-25]. https://arxiv.org/abs/1710.10903.
[15] Li Q, Han Z, Wu X M. Deeper insights into graph convolutional networks for semi-supervised learning[C]//Thirty-Second AAAI Conference on Artificial Intelligence. Palo Alto:Association for the Advancement of Artificial Intelligence, 2018:3538-3545.
[16] Yang Zhilin, Cohen W W, Salakhutdinov R. Revisiting semi-supervised learning with graph embeddings[EB/OL]. 2016(2016-03-29)[2020-06-25]. https://arxiv.org/abs/1603.08861.
[17] Kingma D P, Ba J. Adam:a method for stochastic optimization[EB/OL]. 2014(2014-12-22)[2020-06-25]. https://arxiv.org/abs/1412.6980.
[18] Kim Y. Convolutional neural networks for sentence classification[EB/OL]. 2014(2014-08-25)[2020-06-25]. https://arxiv.org/abs/1408.5882.
[19] Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging[EB/OL]. 2015(2015-08-09)[2020-06-25]. https://arxiv.org/abs/1508.01991.
[20] Joulin A, Grave E, Bojanowski P, et al. Bag of tricks for efficient text classification[EB/OL]. 2016(2016-07-06)[2020-06-25]. https://arxiv.org/abs/1607.01759.
[21] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems. Red Hook:Curran Associates Inc, 2017:6000-6010.

A Social Relationship Direction Gating Algorithm for Graph Convolutional Networks

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	HUANG Yuezhen, DAI Jingguo, ZHANG Chengye, WEI Dong. Face Clustering Algorithm Based on Key Links Discovery of Affinity Graph [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(1): 97-102.
[2]	WANG Wen-zhu, XIAO Bo, CHEN Ke-hong. A Method for Targeted Sentiment Analysis [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(5): 21-27.
[3]	WANG Yi-fei, MO Shuang, WU Wen-rui, FAN Shao-hua, XIAO Ding. Internal-External Convolutional Networks for Network Intrusion Detection [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(5): 94-100.
[4]	ZHANG Dong, CHEN Hong-long. A Lightweight Graph Convolutional Network Recommendation Model Incorporating Text Information [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(5): 88-93.
[5]	XU Jiu-yun, SUN Zhong-shun, ZHANG Ru-ru. Mobile Phone Energy Saving Based on Link Prediction [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(1): 8-13,27.
[6]	DU Cong, ZHANG Zhe, LI Wen-jing, GUO Shao-yong, MENG Luo-ming. Trusted Routing and Forwarding Mechanism Based on Dirichlet Distribution [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(1): 74-79.
[7]	JI Zhen-yan, SONG Xiao-jun, PI Huai-yu, YANG Chun. Recommended Model for Fusing Multi-Source Heterogeneous Data Based on Deep Learning [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(6): 35-42.
[8]	XU Qian-fang, WANG Jia-chun, XIAO Bo. Point-of-Interest Recommendation with Spatio-Temporal Context Awareness [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(1): 37-42,50.
[9]	WANG Qing-shan, TANG Ying, WANG Qi, WANG Di, CHEN Li-jun. Seed-Based Data Dissemination in Campus MSNets [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2017, 40(2): 97-101.
[10]	YANG Xue-cheng, TAO Xiao-bo, YUE Xin. Double Limited and Heterogeneous Social Network: A Simulation and Empirical Study [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2015, 38(s1): 121-124.
[11]	XU Ya-bin, SUN Xiao-chen. Individual Location Recommendation for Location-Based Social Network [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2015, 38(5): 118-124.
[12]	ZHANG Wei-yu, WU Bin, LIU Yang. Integrating Multi-Feature for Link Sign Prediction in Signed Networks [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2014, 37(5): 80-84.
[13]	ZHOU Chao, LI Bo. A Recommendation Method Based on User Trust Network [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2014, 37(4): 98-102.
[14]	LÜ Bo, WU Mu-qing, WANG Dong-yang. A Genetic Algorithm-Based Mobility Model in Social Networks [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2014, 37(1): 112-116.
[15]	CHENG Shi-duan, GUO Liang, WANG Wen-dong. A Survey on Social Search [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2013, 36(1): 1-13.